Learning Outcomes

• Solve the main doubts that the management teams face when implementing a Big Data strategy in their company. Thus, it is covered from a glance to practical cases in terms of exploitation and data analytics.

Learning Content

• Introduction to Big Data
• Data Analysis and Case Studies
• Market Analysis
• Credit Scoring
• Predictive Maintenance

Learning Outcomes

• Learn about the basic concepts of Designing for assembly
• Importance of smooth assembly in design process
• How to design for better insertion, alignment and reduction of parts
• Design features for mistake proofing
• Constraint based design approach
• What are redundant constraints?
• What is Exact, minimum and Over constraint

Learning Content

• Introduction
• Mechanical assembly in Design process
• Principles in design and Rules of thumb
• Case studies in basic Design for assembly
• Introduction to Constraint based design
• Under constraint, Over constraint and Redundancy in assembly
• Exactly constrained designs and Minimum constraints
• Nesting force and constraining in 2D
• Examples in application of concept of constraint based design

Learning Outcomes

• Learn about the fundamentals of the Human-Robot Interaction (HRI) area.
• Understand HRI design concepts such as: interface design, productivity, and safety.
• Study evaluation metrics of HRI systems such as: mission effectiveness, human and robot efficiency, cognitive indicators, among others.
• Understand design concepts such as level of autonomy from the HRI point of view, types of robot models, team structure, among others.
• Design the taxonomy of an HRI system
• Measurement of human factors such as mental and physical work demand, sleepiness, among others.
• Artificial intelligence applications in HRI systems
• Design and evaluate HRI systems for different applications

Learning Content

• Glimpse of the course
• Introduction to robotics
• Introduction to Human-Robot Interaction (HRI)
• HRI system design
• HRI Application Case Study
• Congratulations and farewell from the course

Learning Outcomes

• Learn in-demand skills from university and industry experts
• Master a subject or tool with hands-on projects
• Develop a deep understanding of key concepts
• Earn a career certificate from Northwestern University

Learning Content

• Foundations of Robot Motion
  • Learn fundamental material regarding robot configurations
  • Understand configuration space (C-space), degrees of freedom, and holonomic constraints
  • Represent spatial velocities and forces as twists and wrenches
• Robot Kinematics
  • Solve forward kinematics using the product-of-exponentials formula
  • Study velocity kinematics, statics, and inverse kinematics
  • Analyze robots with closed chains
• Robot Dynamics
  • Learn numerical algorithms for forward and inverse dynamics
  • Plan robot trajectories subject to dynamic constraints
• Robot Motion Planning and Control
  • Plan motions in the presence of obstacles
  • Use feedback control to track planned motions
  • Explore grid-based and randomized motion planning techniques
• Robot Manipulation and Wheeled Mobile Robots
  • Model kinematics and forces for robot grasping tasks
  • Plan and control omnidirectional and nonholonomic wheeled robots
• Capstone Project: Mobile Manipulation
  • Control a mobile manipulator to perform a pick-and-place task
  • Integrate trajectory planning, odometry, and feedback control
  • Test software on the KUKA youBot simulator

Learning Outcomes

• Understand the importance and policies supporting EV battery recycling
• Identify recyclable components in batteries
• Recognize hazards and safety regulations in battery recycling
• Learn dismantling procedures and shredding techniques
• Differentiate between hydrometallurgical, pyrometallurgical, and direct recycling methods

Learning Content

• 10 structured lessons

Learning Outcomes

• Basics of Energy Storage - Storage types, necessity of energy storage, global players, interdependence of Battery performance parameters
• Electrochemistry - Fundamentals of electrochemistry to gain a deeper understanding of later discussed challenges and development goals
• Setup and Performance - Battery component introduction, losses during charging/discharging, and impacts on the performance and lifetime of the Battery
• Cell Components - Detailed analysis of state-of-the-art electrode materials, separator, and electrolyte
• Cell Manufacturing - Essential steps to build a Battery, cell geometries, and safety considerations
• Economics of Battery Storage - Challenges in Battery pricing, supply chains, material supply, development roadmaps, and R&D objectives

Learning Content

• Introduction
• Basics of Energy Storage
• Electrochemistry of Batteries
• Setup and Performance
• Cell Components
• Cell Manufacturing
• Economics of Battery Storage
• Summary

Learning Outcomes

• Distinguish between reversible and irreversible cells
• Identify oxidising and reducing agents
• Explain the standard Emf of a cell
• Describe the relationship between Emf and free energy
• Discuss internal resistance and cells in series
• Explain metal and hydrogen displacement
• Describe the life cycle of a battery
• Discuss the working principle of dry cells
• Explain solar batteries and their importance
• Outline the procedures for battery maintenance

Learning Content

• Module 1 Batteries and Electrochemicals
  • This module explains what batteries and electrochemical are, their constructions and their workings. In this module, you will see the different types of batteries, their compositions, and the various ways in which these batteries work.
• Module 2 Single Electrode Potential
  • This module explains what single electrode potential is all about, its constructions and workings. In this module, you will learn about terms such as Half-cell potential, thermodynamics of a reversible cell
• Module 3 Battery Performance and Maintenance
  • In this module: Battery Performance and Maintenance, you will be introduced to topics such as primary and secondary cells. You will learn about Ni-CD batteries, solar batteries and how they work. The module will also discuss fuel cells in batteries and safety percautions
• Module 4 Course assessment

Learning Outcomes

• Become a Data Scientist and get hired
• Master Machine Learning and use it on the job
• Deep Learning, Transfer Learning and Neural Networks using the latest Tensorflow 2.0
• Use modern tools that big tech companies like Google, Apple, Amazon and Meta use
• Present Data Science projects to management and stakeholders
• Learn which Machine Learning model to choose for each type of problem
• Real life case studies and projects to understand how things are done in the real world
• Learn best practices when it comes to Data Science Workflow
• Implement Machine Learning algorithms
• Learn how to program in Python using the latest Python 3
• How to improve your Machine Learning Models
• Learn to pre process data, clean data, and analyze large data
• Build a portfolio of work to have on your resume
• Developer Environment setup for Data Science and Machine Learning
• Supervised and Unsupervised Learning
• Machine Learning on Time Series data

Learning Content

• Introduction
• Machine Learning and Data Science Framework
• Data Science Environment Setup
• Pandas: Data Analysis
• Matplotlib: Plotting and Data Visualization
• Scikit-learn: Creating Machine Learning Models
• Supervised Learning: Classification + Regression
• Milestone Project 1: Supervised Learning (Classification)
• Milestone Project 2: Supervised Learning (Time Series Data)
• Data Engineering

Learning Outcomes

• Mechatronics systems and their utility
• Interfacing of sensors and transducers
• Interfacing of drive systems with electronic control systems
• Learn in-demand skills from university and industry experts
• Master a subject or tool with hands-on projects
• Develop a deep understanding of key concepts
• Earn a career certificate from L&T EduTech

Learning Content

• Fundamentals of Robotics & Industrial Automation
• Robotics Engineering & Applications
• Industrial Fluid Systems & Smart Factory Automation

Learning Outcomes

• Recognize the need for studying manufacturing in the machining process.
• Discuss the importance of machining.
• Define what manufacturing is in the machining process.
• Classify the types of manufacturing and material removal processes.
• List out the various single and multi-point cutting tool processes.
• Define what the severe plastic deformation process is.
• Discuss the types of chips and the importance of chip breakers.
• Explain the concepts of generatrix and directrix in the turning process.
• Describe the geometry of single-point turning tools.
• Discuss the types of cutting edge angles and the reference systems.
• Identify the various force components in the metal cutting operation.
• Compute various forces using the Merchant Circle Analysis Method.

Learning Content

• Module 1: Introduction and Importance of Machining
  • Importance of machining
  • Types of manufacturing and material removal processes
  • Single and multi-point cutting tool processes
  • Severe plastic deformation process
  • Shear zones
  • Types of chips and the importance of chip breakers
• Module 2: Cutting Tool and Cutting Forces
  • Various types of cutting tools and cutting forces
  • Force components in metal cutting operations
  • Computation of forces using the Merchant Circle Analysis Method
• Module 3: Course Assessment

Learning Outcomes

• How to design equivalent-circuit models for lithium-ion battery cells
• How to implement state-of-charge (SOC) estimators for lithium-ion battery cells
• How to implement state-of-health (SOH) estimators for lithium-ion battery cells
• How to design balancers and power-limits estimators for lithium-ion battery packs
• Learn in-demand skills from university and industry experts
• Master a subject or tool with hands-on projects
• Develop a deep understanding of key concepts
• Earn a career certificate from the University of Colorado System

Learning Content

• Introduction to Battery-Management Systems
  • Understand how lithium-ion battery cells work
  • Understand the requirements of a battery-management system
• Equivalent Circuit Cell Model Simulation
  • How to design equivalent-circuit models for lithium-ion battery cells
• Battery State-of-Charge (SOC) Estimation
  • How to implement state-of-charge (SOC) estimators for lithium-ion battery cells
• Battery State-of-Health (SOH) Estimation
  • How to implement state-of-health (SOH) estimators for lithium-ion battery cells
• Battery Pack Balancing and Power Estimation
  • How to design balancers and power-limits estimators for lithium-ion battery packs
• Evaluating and Communicating Results
  • Analyzing and interpreting the results of battery models
  • Communicating findings to stakeholders
  • Evaluating the effectiveness of battery-management algorithms